Using of genetic programming in engineering
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{babic:2014:etv,
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author = "Matej Babic and Peter Kokol and Igor Belic and
Peter Panjan and Miha Kovacic and Joze Balic",
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title = "Using of genetic programming in engineering",
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journal = "Elektrotehniski vestnik",
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year = "2014",
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volume = "81",
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number = "3",
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pages = "143--147",
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month = jul,
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keywords = "genetic algorithms, genetic programming, engineering,
complex geometry structure",
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ISSN = "0013-5852",
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URL = "http://ev.fe.uni-lj.si/3-2014/Babic.pdf",
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size = "5 pages",
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abstract = "Intelligent systems are process coupled with robotics
in industrial usually settings, though they may be used
as diagnostic systems connected only to passive
sensors. In this paper we use a new method which
combines an intelligent genetic algorithm and multiple
regression to predict the hardness of hardened
specimens. The hardness of a material is an important
mechanical property affecting mechanical properties of
materials. The Microstructures of the hardened
specimens are very complex and cannot be described them
with the classical Euclidian geometry. Thus, we use a
new method, i.e. fractal geometry. By using the method
intelligent-system, genetic programming and multiple
regression, improved production the process
laser-hardening increases because of the decreased time
of the process and, the improved increased
topographical property of the used materials. The
genetic-programming modelling results show a good
agreement with the measured hardness of the hardened
specimens.",
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abstract_si = "Inteligentni sistemi naj bi se po navadi povezali
skupaj z robotiko v nastavitvah industrijskih procesov,
ceprav so lahko sistemi za diagnostiko povezani samo za
pasivne senzorje. Vtem clanku bomo uporabili metodo, ki
zdruzuje inteligentne genetske algoritme in multiplo
regresijo za napoved trdote kaljenih vzorcev. Trdota
materiala je pomembna mehanska lastnost, ki vpliva na
mehanske lastnosti materialov. Mikrostrukture kaljenih
vzorcev so zelo kompleksne in jih ne moremo opisati s
klasicno evklidsko geometrijo. Zato smo uporabili novo
metodo, fraktalno geometrijo. Z metodo inteligentnega
sistema, genetskim programiranjem in multiplo regresijo
smo povecali proizvodnjo pri laserskem kaljenju, saj
smo skrajšali cas procesa in povecali topografsko
lastnost materiala. Rezultati modeliranja genetskega
programiranja se dobro ujemajo z izmerjenimi vrednostmi
trdote kaljenih vzorcev.",
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notes = "http://ev.fe.uni-lj.si/online.html Journal of
Electrical Engineering and Computer Science",
- }
Genetic Programming entries for
Matej Babic
Peter Kokol
Igor Belic
Peter Panjan
Miha Kovacic
Joze Balic
Citations